#git

A new Neptune RAT variant is being shared via YouTube and Telegram, targeting Windows users to steal passwords and deliver additional malware components.

### Summary Using timeit.timeit() function, which is a built-in python library function to execute remote pickle file. ### Details Pickle’s deserialization process is known to allow execution of function via reduce method. While Picklescan is meant to detect such exploits, this attack evades detection by calling built-in python library function like **timeit.timeit()**. And since timeit library wasn't inside unsafe globals blacklist, it may not raise red flag in the security scan. The attack payload executes in the following steps: First, the attacker craft the payload by calling to **timeit.timeit()** function from timeit library in __reduce__ method Then, inside reduce method, the attacker import dangerous libarary like os and calling **os.system()** to run OS commands, for example: curl command. And then the attacker send this malicious pickle file to the victim. Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn...

### Impact When generating an ESTree from a value with a property named `__proto__`, `valueToEstree` would generate an object that specifies a prototype instead. Example: ```js import { generate } from 'astring' import { valueToEstree } from 'estree-util-value-to-estree' const estree = valueToEstree({ ['__proto__']: {} }) const code = generate(estree) console.log(code) ``` Output: ```js { "__proto__": {} } ``` ### Patches This was fixed in version [3.3.3](https://github.com/remcohaszing/estree-util-value-to-estree/releases/tag/v3.3.3). ### Workarounds If you control the input, don’t specify a property named `__proto__`. If you don’t control the output, strip any properties named `__proto__` before passing it to `valueToEstree`.

# Impact ## Summary A vulnerability in Apollo Gateway allowed queries with deeply nested and reused named fragments to be prohibitively expensive to query plan, specifically due to internal optimizations being frequently bypassed. This could lead to excessive resource consumption and denial of service. ## Details The query planner includes an optimization that significantly speeds up planning for applicable GraphQL selections. However, queries with deeply nested and reused named fragments can generate many selections where this optimization does not apply, leading to significantly longer planning times. Because the query planner does not enforce a timeout, a small number of such queries can render gateway inoperable. ## Fix/Mitigation - A new **Query Optimization Limit** metric has been added: - This metric approximates the number of selections that cannot be skipped by the existing optimization. - The metric is checked against a limit to prevent excessive computation. Given...

# Impact ## Summary A vulnerability in Apollo Gateway allowed queries with deeply nested and reused named fragments to be prohibitively expensive to query plan, specifically during named fragment expansion. This could lead to excessive resource consumption and denial of service. ## Details Named fragments were being expanded once per fragment spread during query planning, leading to exponential resource usage when deeply nested and reused fragments were involved. ## Fix/Mitigation A new **Query Fragment Expansion Limit** metric has been introduced: - This metric computes the number of selections a query would have if its fragment spreads were fully expanded. - The metric is checked against a limit to prevent excessive computation. # Patches This has been remediated in `@apollo/gateway` version 2.10.1. # Workarounds No known direct workarounds exist. # References [Query Planning Documentation](https://www.apollographql.com/docs/graphos/reference/federation/query-plans) ...

### Summary import functions are vulnerable. * [importChatflows](https://github.com/FlowiseAI/Flowise/blob/main/packages/server/src/services/chatflows/index.ts#L219) * [importTools](https://github.com/FlowiseAI/Flowise/blob/main/packages/server/src/services/tools/index.ts#L85) * [importVariables](https://github.com/FlowiseAI/Flowise/blob/main/packages/server/src/services/variables/index.ts) ### Details **Authenticated user** can call importChatflows API, import json file such as `AllChatflows.json`. but Due to insufficient validation to chatflow.id in importChatflows API, 2 issues arise. **Issue 1 (Bug Type)** 1. Malicious user creates `AllChatflows.json` file by adding `../` and arbitrary path to the chatflow.id of the json file. ```json { "Chatflows": [ { "id": "../../../../../../apikey", "name": "clickme", "flowData": "{}" } ] } ``` 2. Victim download this file, and import this to flowise. 3. When victim cli...

### Summary An unsafe deserialization vulnerability in Python’s pickle module allows an attacker to bypass static analysis tools like Picklescan and execute arbitrary code during deserialization. This can be exploited by import some built-in function in Numpy library that indrectly call some dangerous function like exec() to execute some python code as a parameter, which the attacker can import dangerous library inside like os library and execute arbitrary OS commands. ### Details Pickle’s deserialization process is known to allow execution of function via **reduce** method. While Picklescan is meant to detect such exploits, this attack evades detection by calling function in Numpy library that call dangerous function like exec(). And since Numpy library wasn't inside unsafe globals blacklist, it may not raise red flag in the security scan. The attack payload executes in the following steps: - First, the attacker craft the payload by import **runstring** function in **numpy.testing._p...

### Summary Picklescan does not detect malicious pickles that exfiltrate sensitive information via DNS after deserialization. ### Details picklescan’s blacklist can be bypassed to exfiltrate sensitive information (like file contents, secrets, or credentials) during model deserialization by leveraging `ssl.get_server_certificate` as the callable function in the pickle payload. Since `ssl` is a standard Python library used for legitimate TLS operations, it is rarely blacklisted by static scanners or runtime monitors. The payload avoids flagged modules and instead uses `linecache` (also unflagged) to read local files. The exfiltrated data is added to DNS-safe chunks, and embedded as subdomains in a crafted FQDN. When passed to `ssl.get_server_certificate`, the Python runtime performs a DNS resolution to the attacker-controlled domain, leaking the encoded content. The payload executes the following steps: - Reads sensitive local file content using `linecache` module - Encodes the d...

A vulnerability was identified in `tarteaucitron.js`, allowing a user with high privileges (access to the site's source code or a CMS plugin) to enter a URL containing an insecure scheme such as `javascript:alert()`. Before the fix, URL validation was insufficient, which could allow arbitrary JavaScript execution if a user clicked on a malicious link. ## Impact An attacker with high privileges could insert a link exploiting an insecure URL scheme, leading to: - Execution of arbitrary JavaScript code - Theft of sensitive data through phishing attacks - Modification of the user interface behavior ## Fix https://github.com/AmauriC/tarteaucitron.js/commit/2fa1e01023bce2e4b813200600bb1619d56ceb02 The issue was resolved by enforcing strict URL validation, ensuring that they start with `http://` or `https://` before being used.

### Summary Jujutsu 0.28.0 and earlier rely on versions of gitoxide that use SHA-1 hash implementations without any collision detection, leaving them vulnerable to hash collision attacks. ### Details This is a result of the underlying [CVE-2025-31130 / GHSA-2frx-2596-x5r6](https://github.com/GitoxideLabs/gitoxide/security/advisories/GHSA-2frx-2596-x5r6) vulnerability in the gitoxide library Jujutsu uses to interact with Git repositories; see that advisory for technical details. This separate advisory is being issued due to the downstream impact on users of Jujutsu. ### Impact An attacker with the ability to mount a collision attack on SHA-1 like the [SHAttered](https://shattered.io/) or [SHA-1 is a Shambles](https://sha-mbles.github.io/) attacks could create two distinct Git objects with the same hash. This is becoming increasingly affordable for well‐resourced attackers, with the Shambles researchers in 2020 estimating $45k for a chosen‐prefix collision or $11k for a classical colli...